Papers Containing Keywords(s): 'employee'
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Viewing papers 1 through 10 of 164
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Working PaperRevisions to the LEHD Establishment Imputation Procedure and Applications to Administrative Job Frame
September 2024
Working Paper Number:
CES-24-51
The Census Bureau is developing a 'job frame' to provide detailed job-level employment data across the U.S. through linked administrative records such as unemployment insurance and IRS W-2 filings. This working paper summarizes the research conducted by the job frame development team on modifying and extending the LEHD Unit-to-Worker (U2W) imputation procedure for the job frame prototype. It provides a conceptual overview of the U2W imputation method, highlighting key challenges and tradeoffs in its current application. The paper then presents four imputation methodologies and evaluates their performance in areas such as establishment assignment accuracy, establishment size matching, and job separation rates. The results show that all methodologies perform similarly in assigning workers to the correct establishment. Non-spell-based methodologies excel in matching establishment sizes, while spell-based methodologies perform better in accurately tracking separation rates.View Full Paper PDF
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Working PaperRevisions to the LEHD Establishment Imputation Procedure and Applications to Administrative Jobs Frame
September 2024
Working Paper Number:
CES-24-51
The Census Bureau is developing a 'jobs frame' to provide detailed job-level employment data across the U.S. through linked administrative records such as unemployment insurance and IRS W-2 filings. This working paper summarizes the research conducted by the jobs frame development team on modifying and extending the LEHD Unit-to-Worker (U2W) imputation procedure for the jobs frame prototype. It provides a conceptual overview of the U2W imputation method, highlighting key challenges and tradeoffs in its current application. The paper then presents four imputation methodologies and evaluates their performance in areas such as establishment assignment accuracy, establishment size matching, and job separation rates. The results show that all methodologies perform similarly in assigning workers to the correct establishment. Non-spell-based methodologies excel in matching establishment sizes, while spell-based methodologies perform better in accurately tracking separation rates.View Full Paper PDF
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Working PaperEmployer Dominance and Worker Earnings in Finance
August 2024
Working Paper Number:
CES-24-41
Large firms in the U.S. financial system achieve substantial economic gains. Their dominance sets them apart while also raising concerns about the suppression of worker earnings. Utilizing administrative data, this study reveals that the largest financial firms pay workers an average of 30.2% more than their smallest counterparts, significantly exceeding the 7.9% disparity in nonfinance sectors. This positive size-earnings relationship is consistently more pronounced in finance, even during the 2008 crisis or compared to the hightech sector. Evidence suggests that large financial firms' excessive gains, coupled with their workers' sought-after skills, explain this distinct relationship.View Full Paper PDF
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Working PaperPayroll Tax Incidence: Evidence from Unemployment Insurance
June 2024
Working Paper Number:
CES-24-35
Economic models assume that payroll tax burdens fall fully on workers, but where does tax incidence fall when taxes are firm-specific and time-varying? Unemployment insurance in the United States has the key feature of varying both across employers and over time, creating the potential for labor demand responses if tax costs cannot be fully passed through to worker wages. Using state policy changes and administrative data of matched employer-employee job spells, I study how employment and earnings respond to unexpected payroll tax increases for highly exposed employers. I find significant drops in employment growth driven by lower hiring, and minimal evidence of passthrough to earnings. The negative employment effects are strongest for young workers and single-establishment firms.View Full Paper PDF
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Working PaperU.S. Worker Mobility Across Establishments within Firms: Scope, Prevalence, and Effects on Worker Earnings
May 2024
Working Paper Number:
CES-24-24
Multi-establishment firms account for around 60% of U.S. workers' primary employers, providing ample opportunity for workers to change their work location without changing their employer. Using U.S. matched employer-employee data, this paper analyzes workers' access to and use of such between-establishment job transitions, and estimates the effect on workers' earnings growth of greater access, as measured by proximity of employment at other within-firm establishments. While establishment transitions are not perfectly observed, we estimate that within-firm establishment transitions account for 7.8% percent of all job transitions and 18.2% of transitions originating from the largest firms. Using variation in worker's establishment locations within their firms' establishment network, we show that having a greater share of the firm's jobs in nearby establishments generates meaningful increases in workers' earnings: a worker at the 90th percentile of earnings gains from more proximate within-firm job opportunities can expect to enjoy 2% higher average earnings over the following five years than a worker at the 10th percentile with the same baseline earnings.View Full Paper PDF
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Working PaperTracking Firm Use of AI in Real Time: A Snapshot from the Business Trends and Outlook Survey
March 2024
Working Paper Number:
CES-24-16
Timely and accurate measurement of AI use by firms is both challenging and crucial for understanding the impacts of AI on the U.S. economy. We provide new, real-time estimates of current and expected future use of AI for business purposes based on the Business Trends and Outlook Survey for September 2023 to February 2024. During this period, bi-weekly estimates of AI use rate rose from 3.7% to 5.4%, with an expected rate of about 6.6% by early Fall 2024. The fraction of workers at businesses that use AI is higher, especially for large businesses and in the Information sector. AI use is higher in large firms but the relationship between AI use and firm size is non-monotonic. In contrast, AI use is higher in young firms although, on an employment-weighted basis, is U-shaped in firm age. Common uses of AI include marketing automation, virtual agents, and data/text analytics. AI users often utilize AI to substitute for worker tasks and equipment/software, but few report reductions in employment due to AI use. Many firms undergo organizational changes to accommodate AI, particularly by training staff, developing new workflows, and purchasing cloud services/storage. AI users also exhibit better overall performance and higher incidence of employment expansion compared to other businesses. The most common reason for non-adoption is the inapplicability of AI to the business.View Full Paper PDF
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Working PaperLow-Wage Jobs, Foreign-Born Workers, and Firm Performance
January 2024
Working Paper Number:
CES-24-05
We examine how migrant workers impact firm performance using administrative data from the United States. Exploiting an unexpected change in firms' likelihood of securing low-wage workers through the H-2B visa program, we find limited crowd-out of other forms of employment and no impact on average pay at the firm. Yet, access to H-2B workers raises firms' annual revenues and survival likelihood. Our results are consistent with the notion that guest worker programs can help address labor shortages without inflicting large losses on incumbent workers.View Full Paper PDF
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Working PaperOutsourcing Dynamism
December 2023
Working Paper Number:
CES-23-64
This paper investigates the increasing importance of domestic outsourcing in U.S. manufacturing. Under domestic outsourcing, the agency is the employer of record for temporary workers, though they perform their tasks at the client business' premises. On a yearly basis, one in two manufacturing plants hires at least some of its workers through a temporary help agency. Furthermore, domestic outsourcing is becoming increasingly more important: the average share of revenue spent on such arrangements has gone up by 85 percent since 2006. We develop a methodology to transform reported expenses on temporary and leased workers into plant-level outsourced employment counts, using administrative data on the U.S. manufacturing sector. We find that domestic outsourcing is an important margin of adjustment that plants use to modify their workforce in response to productivity shocks. Plant-level outsourced employment adjusts more quickly and is twice as responsive as payroll employment. These micro implications have significant aggregate consequences. Without taking reallocations in outsourced employment into account, the measured pace at which jobs reallocate across workplaces is underestimated. On average, we omit the equivalent of 15 percent of payroll employment reallocations in each year. However, outsourced employment churns at a much higher rate compared to its payroll counterpart. Therefore, the omission of outsourced reallocations can rationalize 37 percent of the secular decline in the aggregate job reallocation rate. Lastly, the extent of mismeasurement varies with the business cycle; falling in downturns and increasing in upturns implying that the speed of economic recovery is underestimated.View Full Paper PDF
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Working PaperMixed-Effects Methods For Search and Matching Research
September 2023
Working Paper Number:
CES-23-43
We study mixed-effects methods for estimating equations containing person and firm effects. In economics such models are usually estimated using fixed-effects methods. Recent enhancements to those fixed-effects methods include corrections to the bias in estimating the covariance matrix of the person and firm effects, which we also consider.View Full Paper PDF
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Working PaperLabor Market Segmentation and the Distribution of Income: New Evidence from Internal Census Bureau Data
August 2023
Working Paper Number:
CES-23-41
In this paper, we present new findings that validate earlier literature on the apparent segmentation of the US earnings distribution. Previous contributions posited that the observed distribution of earnings combined two or three distinct signals and was thus appropriately modeled as a finite mixture of distributions. Furthermore, each component in the mixture appeared to have distinct distributional features hinting at qualitatively distinct generating mechanisms behind each component, providing strong evidence for some form of labor market segmentation. This paper presents new findings that support these earlier conclusions using internal CPS ASEC data spanning a much longer study period from 1974 to 2016. The restricted-access internal data is not subject to the same level of top-coding as the public-use data that earlier contributions to the literature were based on. The evolution of the mixture components provides new insights about changes in the earnings distribution including earnings inequality. In addition, we correlate component membership with worker type to provide a tacit link to various theoretical explanations for labor market segmentation, while solving the problem of assigning observations to labor market segments a priori.View Full Paper PDF
